Shruti Mishra@SHRUTI2811
2
Events attended
1
Submissions made
India
1 year of experience
About me
My focus right now sits at the intersection of frontend development and UI/UX design, two areas where I feel most in my element. I enjoy thinking through user problems, translating them into clean interfaces, and then bringing those interfaces to life with code.
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Automated Dead Code Detection
Problem statement- Dead code can contain hidden dependencies, making deletion risky. Developers need to understand its purpose, dependencies, and potential impact before removing it. Solution statement- NeuraNova analyzes dependencies, runtime activity, Git history, tests, and ownership. It combines this evidence to assess risk and recommend whether the code should be deleted. odern software repositories often accumulate *dead code, legacy functions, deprecated helpers, and unused utilities* over time. Although this code may appear unnecessary, developers cannot simply delete it because it may have hidden dependencies, dynamic usage, or historical importance. Manually investigating every piece of potentially unused code is time-consuming and can lead to accidental breakages. As a result, teams often leave unused code in their repositories, increasing *technical debt, maintenance effort, and code complexity. description- Modern software repositories often accumulate *dead code, legacy functions, deprecated helpers, and unused utilities* over time. Although this code may appear unnecessary, developers cannot simply delete it because it may have hidden dependencies, dynamic usage, or historical importance. Manually investigating every piece of potentially unused code is time-consuming and can lead to accidental breakages. As a result, teams often leave unused code in their repositories, increasing *technical debt, maintenance effort, and code complexity. *NeuraNova* addresses this problem through an AI-powered, evidence-based approach to dead-code detection and dependency risk assessment. Instead of relying on a simple “used vs. unused” check, NeuraNova investigates multiple signals before recommending deletion. It analyzes the code structure and dependencies using *AST analysis, checks **test suites and test coverage, examines **Git commit history, considers **runtime usage, and uses **code ownership* information to build a broader picture of how a piece of code is connected to the repository. The target users are *software developers, engineering teams, and organizations* maintaining large or continuously evolving codebases. Developers interact with NeuraNova by submitting or scanning their codebase for potentially unused code. The system analyzes the identified symbols and combines evidence from different sources to generate a *real-time risk assessment. It then provides a clear AI verdict such as *“Safe to Delete”* or *“Needs Review,”** along with the supporting evidence and reasons behind the recommendation. Suggested deletions can also be routed to the responsible developer or code owner for review and approval. What makes NeuraNova unique is its *multi-signal evidence fusion* approach. Traditional dead-code detection may identify code as unused based on limited references, but NeuraNova goes further by investigating dependencies, tests, Git history, runtime signals, and ownership together. This makes the process more like a *“Sherlock Holmes for your codebase”*—investigating the evidence before recommending an action. By replacing guesswork with automated evidence-based analysis, NeuraNova helps developers make faster and more informed cleanup decisions. It reduces the risk of removing important code, minimizes technical debt, improves maintainability, and supports cleaner and more efficient software repositories.
27 Sep 2026
